Abstract

In this contribution we show a novel application of Fourier space analysis to determine overall order of tissue architecture in samples of murine abdominal walls from an animal study of induced peritonitis. Such metrices present a necessary steppingstone in the development of digital histology, facilitating both longitudinal as well as large screening studies through introduction of quantitative and rapidly computable biomarkers. The samples were collected from a study including three different experimental groups receiving different intraperitoneal injections; a control group, which received a phosphate-buffered saline solution, a pure model group, receiving chlorhexidine gluconate to induce peritonitis, and a treated model group, which was additionally administered resolvin D1. We present an approach for analysis of hematoxylin and eosin stained histological slices from this study. First, hyperspectral microscopy images of the slides are acquired, and Beer-Lambert law is used to calculate relative volume fraction maps of both stains. Following this decomposition, a Fourier space representation of the eosin cellular image is calculated. Differences between healthy and diseased subjects, which are attributed to necrosis and edema, are then quantified in the Fourier space through the ellipticity of spatial frequency distribution. The proposed metric is shown to significantly discriminate between the healthy and diseased subject (p=0.02) and is additionally strongly linearly correlated to scattering parameter b on macroscopic scale observed in our previous analysis employing macroscopic imaging of whole abdominal walls (linear correlation coefficient of -0.9).

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